import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM


def 				lowerCAmelCase__       (			a__    ,     a__    ,     a__ )      ->List[Any]:




  '''simple docstring'''

  _UpperCamelCase           =    AutoConfig.from_pretrained(a__ )
  _UpperCamelCase           =    FlaxAutoModelForSeqaSeqLM.from_config(config=a__ )
  _UpperCamelCase           =    checkpoints.load_tax_checkpoint(a__ )

  _UpperCamelCase           =    "wi_0" in tax_model["target"]["encoder"]["layers_0"]["mlp"]

  if config.model_type == "t5":
    _UpperCamelCase           =    "SelfAttention"
  if config.model_type == "longt5" and config.encoder_attention_type == "local":
    _UpperCamelCase           =    "LocalSelfAttention"
  elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
    _UpperCamelCase           =    "TransientGlobalSelfAttention"
  else:
    raise ValueError(
        "Given config is expected to have `model_type='t5'`, or `model_type='longt5` with `encoder_attention_type`"
        " attribute with a value from ['local', 'transient-global]." )

  # Encoder
  for layer_index in range(config.num_layers ):
    _UpperCamelCase           =    f'layers_{str(a__ )}'

    # Self-Attention
    _UpperCamelCase           =    tax_model["target"]["encoder"][layer_name]["attention"]["key"]["kernel"]
    _UpperCamelCase           =    tax_model["target"]["encoder"][layer_name]["attention"]["out"]["kernel"]
    _UpperCamelCase           =    tax_model["target"]["encoder"][layer_name]["attention"]["query"]["kernel"]
    _UpperCamelCase           =    tax_model["target"]["encoder"][layer_name]["attention"]["value"]["kernel"]

    # Global input layer norm
    if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
      _UpperCamelCase           =    tax_model["target"]["encoder"][layer_name]["attention"]["T5LayerNorm_0"]["scale"]

    # Layer Normalization
    _UpperCamelCase           =    tax_model["target"]["encoder"][layer_name]["pre_attention_layer_norm"]["scale"]

    if split_mlp_wi:
      _UpperCamelCase           =    tax_model["target"]["encoder"][layer_name]["mlp"]["wi_0"]["kernel"]
      _UpperCamelCase           =    tax_model["target"]["encoder"][layer_name]["mlp"]["wi_1"]["kernel"]
    else:
      _UpperCamelCase           =    tax_model["target"]["encoder"][layer_name]["mlp"]["wi"]["kernel"]

    _UpperCamelCase           =    tax_model["target"]["encoder"][layer_name]["mlp"]["wo"]["kernel"]

    # Layer Normalization
    _UpperCamelCase           =    tax_model["target"]["encoder"][layer_name]["pre_mlp_layer_norm"]["scale"]

    # Assigning
    _UpperCamelCase           =    flax_model.params["encoder"]["block"][str(a__ )]["layer"]
    _UpperCamelCase           =    tax_attention_key
    _UpperCamelCase           =    tax_attention_out
    _UpperCamelCase           =    tax_attention_query
    _UpperCamelCase           =    tax_attention_value

    _UpperCamelCase           =    tax_attention_layer_norm

    # Global input layer norm
    if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
      _UpperCamelCase           =    tax_global_layer_norm

    if split_mlp_wi:
      _UpperCamelCase           =    tax_mlp_wi_a
      _UpperCamelCase           =    tax_mlp_wi_a
    else:
      _UpperCamelCase           =    tax_mlp_wi

    _UpperCamelCase           =    tax_mlp_wo
    _UpperCamelCase           =    tax_mlp_layer_norm

    _UpperCamelCase           =    flax_model_encoder_layer_block

  # Only for layer 0:
  _UpperCamelCase           =    tax_model["target"]["encoder"]["relpos_bias"]["rel_embedding"].T
  _UpperCamelCase           =    tax_encoder_rel_embedding

  # Side/global relative position_bias + layer norm
  if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
    _UpperCamelCase           =    tax_model["target"]["encoder"]["side_relpos_bias"]["rel_embedding"].T
    _UpperCamelCase           =    tax_encoder_global_rel_embedding

  # Assigning
  _UpperCamelCase           =    tax_model["target"]["encoder"]["encoder_norm"]["scale"]
  _UpperCamelCase           =    tax_encoder_norm

  # Decoder
  for layer_index in range(config.num_layers ):
    _UpperCamelCase           =    f'layers_{str(a__ )}'

    # Self-Attention
    _UpperCamelCase           =    tax_model["target"]["decoder"][layer_name]["self_attention"]["key"]["kernel"]
    _UpperCamelCase           =    tax_model["target"]["decoder"][layer_name]["self_attention"]["out"]["kernel"]
    _UpperCamelCase           =    tax_model["target"]["decoder"][layer_name]["self_attention"]["query"]["kernel"]
    _UpperCamelCase           =    tax_model["target"]["decoder"][layer_name]["self_attention"]["value"]["kernel"]

    # Layer Normalization
    _UpperCamelCase           =    tax_model["target"]["decoder"][layer_name]["pre_self_attention_layer_norm"][
        "scale"
    ]

    # Encoder-Decoder-Attention
    _UpperCamelCase           =    tax_model["target"]["decoder"][layer_name]["encoder_decoder_attention"]
    _UpperCamelCase           =    tax_enc_dec_attention_module["key"]["kernel"]
    _UpperCamelCase           =    tax_enc_dec_attention_module["out"]["kernel"]
    _UpperCamelCase           =    tax_enc_dec_attention_module["query"]["kernel"]
    _UpperCamelCase           =    tax_enc_dec_attention_module["value"]["kernel"]

    # Layer Normalization
    _UpperCamelCase           =    tax_model["target"]["decoder"][layer_name]["pre_cross_attention_layer_norm"]["scale"]

    # MLP
    if split_mlp_wi:
      _UpperCamelCase           =    tax_model["target"]["decoder"][layer_name]["mlp"]["wi_0"]["kernel"]
      _UpperCamelCase           =    tax_model["target"]["decoder"][layer_name]["mlp"]["wi_1"]["kernel"]
    else:
      _UpperCamelCase           =    tax_model["target"]["decoder"][layer_name]["mlp"]["wi"]["kernel"]

    _UpperCamelCase           =    tax_model["target"]["decoder"][layer_name]["mlp"]["wo"]["kernel"]

    # Layer Normalization
    _UpperCamelCase           =    tax_model["target"]["decoder"][layer_name]["pre_mlp_layer_norm"]["scale"]

    # Assigning
    _UpperCamelCase           =    flax_model.params["decoder"]["block"][str(a__ )]["layer"]
    _UpperCamelCase           =    tax_attention_key
    _UpperCamelCase           =    tax_attention_out
    _UpperCamelCase           =    tax_attention_query
    _UpperCamelCase           =    tax_attention_value

    _UpperCamelCase           =    tax_pre_attention_layer_norm

    _UpperCamelCase           =    tax_enc_dec_attention_key
    _UpperCamelCase           =    tax_enc_dec_attention_out
    _UpperCamelCase           =    tax_enc_dec_attention_query
    _UpperCamelCase           =    tax_enc_dec_attention_value

    _UpperCamelCase           =    tax_cross_layer_norm

    if split_mlp_wi:
      _UpperCamelCase           =    tax_mlp_wi_a
      _UpperCamelCase           =    tax_mlp_wi_a
    else:
      _UpperCamelCase           =    tax_mlp_wi

    _UpperCamelCase           =    tax_mlp_wo

    _UpperCamelCase           =    txa_mlp_layer_norm

    _UpperCamelCase           =    flax_model_decoder_layer_block

  # Decoder Normalization
  _UpperCamelCase           =    tax_model["target"]["decoder"]["decoder_norm"]["scale"]
  _UpperCamelCase           =    txa_decoder_norm

  # Only for layer 0:
  _UpperCamelCase           =    tax_model["target"]["decoder"]["relpos_bias"]["rel_embedding"].T
  _UpperCamelCase           =    tax_decoder_rel_embedding

  # Token Embeddings
  _UpperCamelCase           =    tax_model["target"]["token_embedder"]["embedding"]
  _UpperCamelCase           =    txa_token_embeddings

  # LM Head (only in v1.1 and LongT5 checkpoints)
  if "logits_dense" in tax_model["target"]["decoder"]:
    _UpperCamelCase           =    tax_model["target"]["decoder"]["logits_dense"]["kernel"]

  flax_model.save_pretrained(a__ )
  print("T5X Model was sucessfully converted!" )


if __name__ == "__main__":
   lowerCamelCase__  =					argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '''--t5x_checkpoint_path''', default=None, type=str, required=True, help='''Path the T5X checkpoint.'''
   )
   parser.add_argument('''--config_name''', default=None, type=str, required=True, help='''Config name of LongT5/T5 model.''')
   parser.add_argument(
       '''--flax_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output FLAX model.'''
   )
   lowerCamelCase__  =					parser.parse_args()
   convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)





from __future__ import annotations


def 				lowerCAmelCase__       (			a__    ,     a__ )      ->bool:




  '''simple docstring'''

  _UpperCamelCase           =    get_failure_array(a__ )

  # 2) Step through text searching for pattern
  _UpperCamelCase      ,						_UpperCamelCase           =    0, 0  # index into text, pattern
  while i < len(a__ ):
    if pattern[j] == text[i]:
      if j == (len(a__ ) - 1):
        return True
      j += 1

    # if this is a prefix in our pattern
    # just go back far enough to continue
    elif j > 0:
      _UpperCamelCase           =    failure[j - 1]
      continue
    i += 1
  return False


def 				lowerCAmelCase__       (			a__ )      ->list[int]:




  '''simple docstring'''

  _UpperCamelCase           =    [0]
  _UpperCamelCase           =    0
  _UpperCamelCase           =    1
  while j < len(a__ ):
    if pattern[i] == pattern[j]:
      i += 1
    elif i > 0:
      _UpperCamelCase           =    failure[i - 1]
      continue
    j += 1
    failure.append(a__ )
  return failure


if __name__ == "__main__":
   # Test 1)
   lowerCamelCase__  =					'''abc1abc12'''
   lowerCamelCase__  =					'''alskfjaldsabc1abc1abc12k23adsfabcabc'''
   lowerCamelCase__  =					'''alskfjaldsk23adsfabcabc'''
   assert kmp(pattern, texta) and not kmp(pattern, texta)

   # Test 2)
   lowerCamelCase__  =					'''ABABX'''
   lowerCamelCase__  =					'''ABABZABABYABABX'''
   assert kmp(pattern, text)

   # Test 3)
   lowerCamelCase__  =					'''AAAB'''
   lowerCamelCase__  =					'''ABAAAAAB'''
   assert kmp(pattern, text)

   # Test 4)
   lowerCamelCase__  =					'''abcdabcy'''
   lowerCamelCase__  =					'''abcxabcdabxabcdabcdabcy'''
   assert kmp(pattern, text)

   # Test 5)
   lowerCamelCase__  =					'''aabaabaaa'''
   assert get_failure_array(pattern) == [0, 1, 0, 1, 2, 3, 4, 5, 2]




